Tool-Augmented LLMs as a Universal Interface for IDEs
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
Modern-day Integrated Development Environments (IDEs) have come a long way from the early text editing utilities to the complex programs encompassing thousands of functions to help developers. However, with the increasing number of efficiency-enhancing tools incorporated, IDEs gradually became sophisticated software with a steep learning curve. The rise of the Large Language Models (LLMs) capable of both natural language dialogue and code generation leads to a discourse on the obsolescence of the concept of IDE. In this work, we offer a view on the place of the LLMs in the IDEs as the universal interface wrapping the IDE facilities. We envision a model that is able to perform complex actions involving multiple IDE features upon user command, stripping the user experience of the tedious work involved in searching through options and actions. For the practical part of the work, we engage with the works exploring the ability of LLMs to call for external tools to expedite a given task execution. We showcase a proof-of-concept of such a tool.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 7 of its 16 references that are in the map. First posted on arXiv 18 Feb 2024.
Builds on
7 of 16 references.
Closest ancestors · 2
Same direction, newest first
- 2023Gorilla: Large Language Model Connected with Massive APIs24 May
- 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb
Other references · 5
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
- 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
- 2023ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Searchcited by 103
- 2022CLAM: Selective Clarification for Ambiguous Questions with Generative Language Modelscited by 66
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- 2026The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
- 2024Achieving Tool Calling Functionality in LLMs Using Only Prompt Engineering Without Fine-Tuning
- 2026Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives